Search Results for author: Alex Alemi

Found 6 papers, 3 papers with code

Dueling Decoders: Regularizing Variational Autoencoder Latent Spaces

no code implementations17 May 2019 Bryan Seybold, Emily Fertig, Alex Alemi, Ian Fischer

Variational autoencoders learn unsupervised data representations, but these models frequently converge to minima that fail to preserve meaningful semantic information.

An information-theoretic analysis of deep latent-variable models

no code implementations ICLR 2018 Alex Alemi, Ben Poole, Ian Fischer, Josh Dillon, Rif A. Saurus, Kevin Murphy

We present an information-theoretic framework for understanding trade-offs in unsupervised learning of deep latent-variables models using variational inference.

Variational Inference

TensorFlow Distributions

9 code implementations28 Nov 2017 Joshua V. Dillon, Ian Langmore, Dustin Tran, Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matt Hoffman, Rif A. Saurous

The TensorFlow Distributions library implements a vision of probability theory adapted to the modern deep-learning paradigm of end-to-end differentiable computation.

Probabilistic Programming

Watch Your Step: Learning Node Embeddings via Graph Attention

2 code implementations NeurIPS 2018 Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou, Alex Alemi

Graph embedding methods represent nodes in a continuous vector space, preserving information from the graph (e. g. by sampling random walks).

Graph Attention Graph Embedding +2

Motion Prediction Under Multimodality with Conditional Stochastic Networks

no code implementations5 May 2017 Katerina Fragkiadaki, Jonathan Huang, Alex Alemi, Sudheendra Vijayanarasimhan, Susanna Ricco, Rahul Sukthankar

In this work, we present stochastic neural network architectures that handle such multimodality through stochasticity: future trajectories of objects, body joints or frames are represented as deep, non-linear transformations of random (as opposed to deterministic) variables.

motion prediction Optical Flow Estimation +2

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

53 code implementations23 Feb 2016 Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi

Recently, the introduction of residual connections in conjunction with a more traditional architecture has yielded state-of-the-art performance in the 2015 ILSVRC challenge; its performance was similar to the latest generation Inception-v3 network.

Frame General Classification +1

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